# Financial Risk Management on a Neutral Atom Quantum Processor

arXiv: https://arxiv.org/abs/2212.03223 · HTML: https://mlsanity.com/paper/2212.03223/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/2007.00017/) (2020)
- [Simulated Quantum Annealing Can Be Exponentially Faster than Classical Simulated Annealing](https://mlsanity.com/paper/1601.03030/) (2016)
- [Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series](https://mlsanity.com/paper/2108.03190/) (2021)
- [Quantum-Inspired Tensor Neural Networks for Partial Differential Equations](https://mlsanity.com/paper/2208.02235/) (2022)
- [Developing Quantum Annealer Driven Data Discovery](https://mlsanity.com/paper/1603.07980/) (2016)
- [Variational Quantum and Quantum-Inspired Clustering](https://mlsanity.com/paper/2206.09893/) (2022)

## Built on by (most cited first, 4 of 4)

- [Quantum Deep Hedging](https://mlsanity.com/paper/2303.16585/) (2023)
- [Improved Financial Forecasting via Quantum Machine Learning](https://mlsanity.com/paper/2306.12965/) (2023)
- [LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks](https://mlsanity.com/paper/2412.03158/) (2024)
- [Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise](https://mlsanity.com/paper/2507.17043/) (2025)

## Abstract (arXiv)

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.